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A Local Betweenness Centrality Based Forwarding Technique for Social Opportunistic IoT Networks

Abstract

Social Opportunistic IoT (Social OppIoT) networks is a subclass of Social Internet of Things (SIoT) networks. In social OppIoT, users perform communication in a distributed manner using smart devices by regularly moving around without any communication infrastructures, making routing a strenuous process due to its highly fragile connection intermittency and device mobility. Moreover, due to the growing heterogeneous devices, problems can exist in searching for the right relay node from a massive number of devices. In this paper, a novel forwarding scheme named “A Local Betweenness Centrality Based Forwarding Technique for Social Opportunistic IoT Networks” (LBCFT) has been proposed, which uses a reduction strategy to discard the inefficient devices. LBCFT introduces a new centrality measurement called local betweenness centrality to form the significant overlapping communities of network devices to boost forwarding. Message dissemination is controlled by handling inefficient devices using a reduction strategy, which includes the node’s trajectory and intra-community and inter-community centrality. The performance of LBCFT is evaluated through ONE Simulator against the existing analogous ideologies like Supernode, Geo-Routing with Angle-based Decision (GRAD), and the benchmark protocols BubbleRap, as well as PROPHET. The simulation results show that the proposed LBCFT protocol, on average, outperforms Supernode, GRAD, BubbleRap, and PROPHET by 5.22%, 36.51%, 64.12%, and 57.96% respectively, in terms of the delivery probability.

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References

  1. 1.

    Alvarez-Socorro A, Herrera-Almarza G, González-Díaz L. (2015) Eigencentrality based on dissimilarity measures reveals central nodes in complex networks. Scientific Reports 5:17095

    Article  Google Scholar 

  2. 2.

    Cao Y, Kaiwartya O, Aslam N, Han C, Zhang X, Zhuang Y, Dianati M (2018) A trajectory-driven opportunistic routing protocol for vcps. IEEE Trans Aerosp Electron Syst 54(6):2628–2642

    Article  Google Scholar 

  3. 3.

    Conti M, Giordano S, May M, Passarella A (2010) From opportunistic networks to opportunistic computing. IEEE Commun Mag 48(9):126–139

    Article  Google Scholar 

  4. 4.

    Dhurandher SK, Borah S, Woungang I, Sharma DK, Arora K, Agarwal D (2016) Edr: An encounter and distance based routing protocol for opportunistic networks. In: 2016 IEEE 30th International conference on advanced information networking and applications (AINA). IEEE, pp 297–302

  5. 5.

    Dhurandher SK, Sharma DK, Woungang I, Saini A (2017) An energy-efficient history-based routing scheme for opportunistic networks. Int J Commun Syst 30(7):e2989

    Article  Google Scholar 

  6. 6.

    Dougnon RY, Fournier-Viger P, Lin JCW, Nkambou R (2016) Inferring social network user profiles using a partial social graph. J Intell Inf Sys 47(2):313–344

    Article  Google Scholar 

  7. 7.

    Epiphaniou G, Karadimas P, Ismail DKB, Al-Khateeb H, Dehghantanha A, Choo KKR (2017) Nonreciprocity compensation combined with turbo codes for secret key generation in vehicular ad hoc social iot networks. IEEE Internet of Things Journal 5(4):2496–2505

    Article  Google Scholar 

  8. 8.

    Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry, pp 35–41

  9. 9.

    Guan P, Wu J (2019) Effective data communication based on social community in social opportunistic networks. IEEE Access 7:12405–12414

    Article  Google Scholar 

  10. 10.

    Guo B, Zhang D, Wang Z, Yu Z, Zhou X (2013) Opportunistic iot: Exploring the harmonious interaction between human and the internet of things. J Netw Comput Appl 36(6):1531–1539

    Article  Google Scholar 

  11. 11.

    Guo Z, Yu K, Li Y, Srivastava G, Lin JCW (2021) Deep learning-embedded social internet of things for ambiguity-aware social recommendations. IEEE Trans. Netw. Sci. Eng.

  12. 12.

    Hui P, Crowcroft J, Yoneki E (2011) Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Trans Mob Comput 10(11):1576–1589

    Article  Google Scholar 

  13. 13.

    Jiang F, Fu Y, Gupta BB, Lou F, Rho S, Meng F, Tian Z (2018) Deep learning based multi-channel intelligent attack detection for data security IEEE transactions on Sustainable Computing

  14. 14.

    K Dhurandher S, Woungang I, Arora J, Gupta H, et al. (2016) History-based secure routing protocol to detect blackhole and greyhole attacks in opportunistic networks. Recent Advances in Communications and Networking Technology (Formerly Recent Patents on Telecommunication) 5(2):73–89

    Google Scholar 

  15. 15.

    Kumaram S, Srivastava S, Sharma DK (2020) Neural network-based routing protocol for opportunistic networks with intelligent water drop optimization. Int J Commun Syst e4368

  16. 16.

    Levandowsky M, Winter D (1971) Distance between sets. Nature 234(5323):34–35

    Article  Google Scholar 

  17. 17.

    Lin CY, Chung JY, Li CT, Hu CL, Lien YN (2017) Geo-routing with angle-based decision in delay-tolerant networks. In: 2017 10th International Conference on Ubi-media Computing and Workshops (Ubi-Media). IEEE, pp 1–5

  18. 18.

    Lin JCW, Shao Y, Djenouri Y, Yun U (2021) Asrnn: a recurrent neural network with an attention model for sequence labeling. Knowl.-Based Syst 212:106548

    Article  Google Scholar 

  19. 19.

    Lindgren A, Doria A, Schelen O (2003) Probabilistic routing in intermittently connected networks: 7(3) l

  20. 20.

    Lu L, Zhang M (2013) Edge betweenness centrality. Encyclopedia of systems biology, pp 647–648

  21. 21.

    Mtibaa A, May M, Diot C, Ammar M (2010) Peoplerank: Social opportunistic forwarding. In: Infocom, 2010 Proceedings IEEE. IEEE, pp 1–5

  22. 22.

    Nitti M, Girau R, Atzori L, Iera A, Morabito G (2012) A subjective model for trustworthiness evaluation in the social internet of things. In: 2012 IEEE 23rd international symposium on personal, indoor and mobile radio communications-(PIMRC). IEEE, pp 18–23

  23. 23.

    Sakuma J, Kobayashi S, Wright RN (2008) Privacy-preserving reinforcement learning. In: Proceedings of the 25th international conference on machine learning, pp. 864–871

  24. 24.

    Sharma DK, Dhurandher SK, Agarwal D (2018) Arora, K.: krop: k-means clustering based routing protocol for opportunistic networks. J Ambient Intell Humaniz Comput, pp 1–18

  25. 25.

    Sharma DK, Dhurandher SK, Woungang I, Srivastava RK, Mohananey A, Rodrigues JJ (2016) A machine learning-based protocol for efficient routing in opportunistic networks. IEEE Syst J 12 (3):2207–2213

    Article  Google Scholar 

  26. 26.

    Sharma DK, Kukreja D, Chugh S, Kumaram S (2019) Supernode routing: a grid-based message passing scheme for sparse opportunistic networks. J Ambient Intell Humaniz Comput 10(4):1307–1324

    Article  Google Scholar 

  27. 27.

    Singh AV, Juyal V, Saggar R (2017) Trust based intelligent routing algorithm for delay tolerant network using artificial neural network. Wirel Netw 23(3):693–702

    Article  Google Scholar 

  28. 28.

    Vashishth V, Chhabra A, Sharma DK (2019) Gmmr: A gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic iot networks. Comput Commun 134:138–148

    Article  Google Scholar 

  29. 29.

    Wang J, Jiang C, Quek TQ, Ren Y (2016) The value strength aided information diffusion in online social networks. In: 2016 IEEE Global conference on signal and information processing (GlobalSIP). IEEE, pp 470–474

  30. 30.

    Wang J, Jiang C, Quek TQ, Wang X, Ren Y (2016) The value strength aided information diffusion in socially-aware mobile networks. IEEE Access 4:3907–3919

    Article  Google Scholar 

  31. 31.

    Yan Y, Chen Z, Wu J, Wang L, Liu K, Wu Y (2019) Effective data transmission strategy based on node socialization in opportunistic social networks. IEEE Access 7:22144–22160

    Article  Google Scholar 

  32. 32.

    Zhao R, Wang X, Zhang L, Lin Y (2017) A social-aware probabilistic routing approach for mobile opportunistic social networks. Transactions on Emerging Telecommunications Technologies 28(12):e3230

    Article  Google Scholar 

  33. 33.

    Zhu D, Xu Z, Xu X, Zhao Q, Qi L, Srivastava G (2021) Cognitive analytics of social media services for edge resource pre-allocation in industrial manufacturing. IEEE Trans Comput Soc Sys 8 (2):500–511

    Article  Google Scholar 

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Correspondence to Gautam Srivastava.

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Nigam, R., Sharma, D.K., Jain, S. et al. A Local Betweenness Centrality Based Forwarding Technique for Social Opportunistic IoT Networks. Mobile Netw Appl (2021). https://doi.org/10.1007/s11036-021-01820-7

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Keywords

  • Edge betweenness centrality
  • Node dissimilarity
  • IoT infrastructure
  • Smart devices
  • Social opportunistic IoT networks